DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is configuring an AWS Glue ETL job that reads semi-structured JSON event logs from Amazon S3 and must flatten nested arrays into relational columns before writing to Amazon Redshift. The job must run reliably without writing custom serialization code. Which approach should the data engineer take?
⚠ Common exam trap
The trap here is assuming that any Glue transform can flatten nested JSON, when only Relationalize is designed to explode arrays and structs into related relational tables.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the AWS Glue DynamicFrame Relationalize transform to convert nested structures into separate relational tables before loading into Redshift.
The Relationalize transform is the AWS Glue built-in that flattens nested JSON into a root table and child tables linked by join keys, precisely the normalization needed before loading into a relational target like Redshift. It removes the need to hand-code serialization, keeping the pipeline maintainable and aligned with serverless Glue patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the AWS Glue DynamicFrame Relationalize transform to convert nested structures into separate relational tables before loading into Redshift.
Why this is correct
Relationalize is purpose-built for flattening nested JSON into a set of related tables, handling arrays and structs automatically without custom code. It produces a root table plus child tables keyed by generated join identifiers, which can then be written to Redshift. This matches the requirement to avoid custom serialization.
- ✗
Enable the AWS Glue job bookmark and rely on the crawler's inferred schema to automatically normalize nested JSON during the write to Redshift.
Why it's wrong here
Job bookmarks only track processed data to avoid reprocessing; they do not transform or normalize nested structures. The Glue crawler infers schema but does not flatten arrays into relational tables, so the nested JSON would remain nested and fail when written to Redshift columns.
- ✗
Apply the AWS Glue ApplyMapping transform to rename nested fields and then write the DynamicFrame directly to Redshift.
Why it's wrong here
ApplyMapping only renames, casts, or drops existing fields; it does not explode arrays or flatten nested structs into separate relational tables. Writing the DynamicFrame directly would still leave nested types that Redshift cannot store as native columns, so this does not satisfy the flattening requirement.
- ✗
Convert each JSON file to CSV using an AWS Lambda function triggered by S3 event notifications before the Glue job runs.
Why it's wrong here
A Lambda-based conversion would require writing and maintaining custom parsing logic for nested arrays and structs, which contradicts the goal of avoiding custom serialization code. It also introduces an extra moving part and latency, and CSV cannot natively represent nested structures without losing hierarchy.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.